Exploring the usage of thermal imaging for understanding video lecture designs and students' experiences

Video is becoming a dominant medium for the delivery of educational material. Despite the widespread use of video for learning, there is still a lack of understanding about how best to help people learn in this medium. This study demonstrates the use of thermal camera as compared to traditional self-reported methods for assessing learners' cognitive load while watching video lectures of different styles. We evaluated our approach in a study with 78 university students viewing two variants of short video lectures on two different topics. To incorporate subjective measures, the students reported on mental effort, interest, prior knowledge, confidence, and challenge. Moreover, through a physical slider device, the students could continuously report on their perceived level of difficulty. Lastly, we used thermal sensor as an additional indicator of students' level of difficulty and associated cognitive load. This was achieved through, continuous real-time monitoring of students by using a thermal imaging camera. This study aims to address the following: firstly, to analyze if video styles differ in terms of the associated cognitive load. Secondly, to assess the effects of cognitive load on learning outcomes; could an increase in the cognitive load be associated with poorer learning outcomes? Third, to see if there is a match between students' perceived difficulty levels and a biological indicator. The results suggest that thermal imaging could be an effective tool to assess learners' cognitive load, and an increased cognitive load could lead to poorer performance. Moreover, in terms of the lecture styles, the animated video lectures appear to be a better tool than the text-only lectures (in the content areas tested here). The results of this study may guide future works on effective video designs, especially those that consider the cognitive load.

[1]  Michelle Cook Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles , 2006 .

[2]  Youngjun Cho,et al.  Physiological and Affective Computing through Thermal Imaging: A Survey , 2019, ArXiv.

[3]  Louis-Philippe Morency,et al.  OpenFace 2.0: Facial Behavior Analysis Toolkit , 2018, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[4]  Yang Wang,et al.  Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks , 2012, OZCHI.

[5]  Katherine Kensinger Rose,et al.  Student Perceptions of the Use of Instructor-Made Videos in Online and Face-to-Face Classes , 2009 .

[6]  Ioannis T. Pavlidis,et al.  StressCam: non-contact measurement of users' emotional states through thermal imaging , 2005, CHI Extended Abstracts.

[7]  Xin Zhao,et al.  An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task , 2014, Front. Hum. Neurosci..

[8]  Alexander Renkl,et al.  Learning from direct instruction: Best prepared by several self-regulated or guided invention activities? , 2017 .

[9]  Mary Ann Affleck,et al.  Stress and Job Performance: Theory, Research, and Implications for Managerial Practice , 1999 .

[10]  Andreas Bulling,et al.  Classifying Attention Types with Thermal Imaging and Eye Tracking , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[11]  René F. Kizilcec,et al.  The instructor’s face in video instruction: Evidence from two large-scale field studies. , 2015, Journal of Educational Psychology.

[12]  Gregor Kennedy,et al.  Making Sense of Audit Trail Data. , 2004 .

[13]  Shirley Williams,et al.  MOOCs: A systematic study of the published literature 2008-2012 , 2013 .

[14]  Konstantinos Chorianopoulos,et al.  Investigating Differences among the Commonly Used Video Lecture Styles , 2013 .

[15]  Robin H. Kay,et al.  Exploring the use of video podcasts in education: A comprehensive review of the literature , 2012, Comput. Hum. Behav..

[16]  F. Paas,et al.  Variability of Worked Examples and Transfer of Geometrical Problem-Solving Skills: A Cognitive-Load Approach , 1994 .

[17]  M. Boekaerts,et al.  Cognitive load and self-regulation: Attempts to build a bridge , 2017 .

[18]  André Tricot,et al.  Strategies used by humans to reduce their own cognitive load , 2008 .

[19]  F. Paas,et al.  Cognitive Load Measurement as a Means to Advance Cognitive Load Theory , 2003 .

[20]  N. Caltabiano,et al.  ONLINE LEARNING: CAN VIDEOS ENHANCE LEARNING? , 2015 .

[21]  R. Mayer,et al.  Nine Ways to Reduce Cognitive Load in Multimedia Learning , 2003 .

[22]  G. Kennedy,et al.  All roads lead to Rome: Tracking students’ affect as they overcome misconceptions , 2016 .

[23]  Fred G. W. C. Paas,et al.  The Efficiency of Instructional Conditions: An Approach to Combine Mental Effort and Performance Measures , 1992 .

[24]  Philip J. Guo,et al.  How video production affects student engagement: an empirical study of MOOC videos , 2014, L@S.

[25]  Clinton Fookes,et al.  Detecting changes in facial temperature induced by a sudden auditory stimulus based on deep learning-assisted face tracking , 2019, Scientific Reports.

[26]  P. Chandler,et al.  Cognitive Load Theory and the Format of Instruction , 1991 .

[27]  Alfred Bork,et al.  Multimedia in Learning , 2001 .

[28]  Jodi Forlizzi,et al.  Psycho-physiological measures for assessing cognitive load , 2010, UbiComp.

[29]  Liesbeth Kester,et al.  Cognitive load theory and multimedia learning, task characteristics and learning engagement: The Current State of the Art , 2011, Comput. Hum. Behav..

[30]  James Bailey,et al.  Continuous Evaluation of Video Lectures from Real-Time Difficulty Self-Report , 2019, CHI.

[31]  Michael Cole,et al.  Readings on the Development of Children , 1993 .

[32]  Jorge Gonçalves,et al.  Cognitive Aid: Task Assistance Based On Mental Workload Estimation , 2019, CHI Extended Abstracts.

[33]  Lori Lockyer,et al.  Understanding Difficulties and Resulting Confusion in Learning: An Integrative Review , 2018, Front. Educ..

[34]  Shane Dawson,et al.  Designing videos for learning: separating the good from the bad and the ugly , 2017 .

[35]  Arcangelo Merla,et al.  Exploring the Use of Thermal Infrared Imaging in Human Stress Research , 2014, PloS one.

[36]  V. Gallese,et al.  Thermal infrared imaging in psychophysiology: Potentialities and limits , 2014, Psychophysiology.

[37]  Michail N. Giannakos,et al.  Usability design for video lectures , 2013, EuroITV.

[38]  F. Vetere,et al.  Cognitive Heat , 2017 .

[39]  R. Mayer,et al.  Aids to computer-based multimedia learning , 2002 .

[40]  Piet Desmet,et al.  Multichannel data for understanding cognitive affordances during complex problem solving , 2019, LAK.

[41]  Jason M. Lodge,et al.  Capturing dynamic presentation: Using technology to enhance the chalk and the talk , 2013 .

[42]  J. Sweller,et al.  Cognitive load theory in health professional education: design principles and strategies , 2010, Medical education.

[43]  Fred Paas,et al.  Effects of performance feedback valence on perceptions of invested mental effort , 2017 .

[44]  John Sweller,et al.  Cognitive Load Theory , 2020, Encyclopedia of Education and Information Technologies.

[45]  M. Schatz,et al.  Exploring University Students' Engagement with Online Video Lectures in a Blended Introductory Mechanics Course , 2016, 1603.03348.

[46]  Peter Tiernan,et al.  An inquiry into the current and future uses of digital video in University teaching , 2015, Education and Information Technologies.

[47]  Mark Bullen,et al.  What is Instructional Design , 2014 .

[48]  F. Paas Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. , 1992 .

[49]  R. Mayer,et al.  Engaging students in active learning: The case for personalized multimedia messages. , 2000 .

[50]  L. Vygotsky Interaction between learning and development , 1978 .